Project: End-to-End Image Classification

A deep learning course that stops at concepts leaves the job half done. Between "I understand what a convolution is" and "I shipped a classifier whose weaknesses I know," there is a gap that only a complete project can close. That gap is made of things no theory lesson mentions: how to split a dataset without leakage, where to put augmentation, what to save and when, how to read a confusion matrix, and above all how to tell whether the result you got is any good.

This project builds a classifier on CIFAR-10: sixty thousand 32 × 32 pixel color photographs, split into ten classes. The dataset is small, public, downloadable with a single command, and difficult enough that the model's errors are instructive — a cat and a dog that are thirty-two pixels on a side really can be confused, even by a human.

Most of the work is not the architecture. The model proposed here fits in forty lines and is nothing original. What matters, and what we detail, is the protocol around it: the three-way split, reproducibility, saving the best state, and the final analysis. A good protocol with a mediocre model gives a usable result; a sophisticated model in a faulty protocol gives a number that means nothing.